{"id":"W4310023372","doi":"10.46873/2300-3960.1369","title":"Predicting the stability of open stopes using Machine Learning","year":2022,"lang":"en","type":"article","venue":"Journal of Sustainable Mining","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Stability (learning theory); Logistic regression; Excavation; Random forest; Machine learning; Open-pit mining; Graph; Artificial intelligence; Computer science; Engineering; Algorithm; Mining engineering; Geotechnical engineering; Theoretical computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008523518,0.0006181541,0.0004995388,0.005019223,0.0003021147,0.0007765886,0.0006353224,0.0007241057,0.001035042],"category_scores_gemma":[0.003460778,0.0002048443,0.0008127247,0.002461507,0.0003238453,0.001105349,0.0004085629,0.0003447717,0.000525233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004619648,"about_ca_system_score_gemma":0.0003196899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005339719,"about_ca_topic_score_gemma":0.006949868,"domain_scores_codex":[0.9993901,0.00009092844,0.00006740705,0.0001742274,0.0002046733,0.00007256211],"domain_scores_gemma":[0.9975178,0.001186223,0.0005340633,0.0001551566,0.0005132903,0.00009342329],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003352359,0.0001985654,0.3366177,0.0002500955,0.0001826166,0.0007473098,0.0002198166,0.3882474,0.007822463,0.001530457,0.0021284,0.26172],"study_design_scores_gemma":[0.000008135393,0.0001236496,0.06759157,0.00002766472,0.00003922856,0.000163189,0.0002429819,0.9251994,0.003972817,0.001457285,0.001145863,0.0000281114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8508987,0.0006662891,0.1435777,0.0001172554,0.00003532816,0.0000692264,0.001651064,0.0006662729,0.002318208],"genre_scores_gemma":[0.9785025,0.0001473016,0.01921948,0.000005372752,0.00001078298,0.00002301056,0.001453107,0.00001951501,0.0006190881],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005339719,"threshold_uncertainty_score":0.01061726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02753647766045383,"score_gpt":0.2606991879774628,"score_spread":0.2331627103170089,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}